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results <- readRDS('./data/real/full_results_pipeline.rds')

single.ds <- lapply(results, function(res) {
  res %>%
    subset(Dataset == "BNU1")
  })

Statistic vs Performance

Statistic on the graphs, vs. MGC/Dcorr for age (regression) and sex (classification) task on the graphs embedded using the strategy indicated

stat.vs.perf <- merge(results$stat, results$prob, by=c("Reg", "FF", "Scr", "GSR", "Parcellation", "xfm", "Dataset"))

lapply(unique(stat.vs.perf$Dataset), function(ds) {
  lapply(unique(stat.vs.perf$alg), function(al) {
  stat.vs.perf %>%
    subset(alg == al & Dataset == ds) %>%
  ggplot(aes(x=stat.x, y=stat.y, color=xfm)) +
  geom_point() +
  facet_grid(task ~ embed, scales="free_y") +
  theme_bw() +
  xlab("Discriminability") +
  ylab("Task Performance") +
  ggtitle(sprintf("Alg=%s, Dataset=%s", as.character(al), as.character(ds)))
})})
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Statistic vs MGC/Dcorr

Statistic on the graphs, vs. MGC/Dcorr for age (regression) and sex (classification) task on the graphs

stat.vs.perf <- merge(results$stat, results$dcor, by=c("Reg", "FF", "Scr", "GSR", "Parcellation", "xfm", "Dataset"))

lapply(unique(stat.vs.perf$Dataset), function(ds) {
  lapply(unique(stat.vs.perf$alg), function(al) {
  stat.vs.perf %>%
    subset(alg == al & Dataset == ds) %>%
  ggplot(aes(x=stat.x, y=stat.y, color=xfm)) +
  geom_point() +
  facet_grid(task ~ method, scales="free_y") +
  theme_bw() +
  xlab("Discriminability") +
  ylab("Task Performance") +
  ggtitle(sprintf("Alg=%s, Dataset=%s", as.character(al), as.character(ds)))
})})
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